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INT396 Units

Practice

UNSUPERVISED LEARNING

INT396 3 Credits L:2 T:0 P:2 Speciality

This course covers fundamental concepts and algorithms of unsupervised learning, including clustering, dimensionality reduction, and anomaly detection, along with applying these techniques on real-world datasets using Python frameworks.

Study Units

Unit 1

Foundations of Unsupervised Learning

Notes MCQ Subjective
Unit 2

Partition-Based Clustering

Notes MCQ Subjective
Unit 3

Hierarchical & Density-Based Clustering

Notes MCQ Subjective
Unit 4

Dimensionality Reduction and Representation Learning

Notes MCQ Subjective
Unit 5

Association Rule Mining & Anomaly Detection: Association Rule Mining

Notes MCQ Subjective
Unit 6

Evaluation and Applications of Unsupervised Learning

Notes MCQ Subjective

Continuous Assessment

1 component

NA 0%

NA

Week NA

Exams & Practice

Mid Term Examination

Mid-semester comprehensive evaluation

20%
Coming Soon

End Term Examination

Final semester comprehensive evaluation

50%

Type: Examination

Coming Soon

INT396 - FAQs

How many units are in INT396?

INT396 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for INT396?

Unit-wise notes and MCQ practice are available. Exam resources coming soon.

How to prepare for INT396 exams?

Study each unit's notes thoroughly, practice MCQs to test understanding, and attempt mock tests before exams. Focus on important topics and previous year questions.